Break-junction measurements are typically aimed at characterizing electronic properties of single molecules bound between two metal electrodes. Although these measurements have provided structure-function relationships for such devices, there is little work that studies the impact of molecule-molecule interactions on junction characteristics. Here, we use a scanning tunneling microscope based break-junction technique to study pi-stacked dimer junctions formed with two amine-terminated conjugated molecules. We show that the conductance, force and flicker noise of such dimers differ dramatically when compared with the corresponding monomer junctions and discuss the implications of these results on intra- and inter-molecular charge transport.

Single-molecule break junction measurements deliver a huge number of conductance vs. electrode separation traces. Along such measurements the target molecules may bind to the electrodes in different geometries, and the evolution and rupture of the single-molecule junction may also follow distinct trajectories. The unraveling of the various typical trace classes is a prerequisite of the proper physical interpretation of the data. Here we exploit the efficient feature recognition properties of neural networks to automatically find the relevant trace classes. To eliminate the need for manually labeled training data we apply a combined method, which automatically selects training traces according to the extreme values of principal component projections or some auxiliary measured quantities, and then the network captures the features of these characteristic traces, and generalizes its inference to the entire dataset. The use of a simple neural network structure also enables a direct insight to the decision making mechanism. We demonstrate that this combined machine learning method is efficient in the unsupervised recognition of unobvious, but highly relevant trace classes within low and room temperature gold-4,4' bipyridine-gold single molecule break junction data. arXiv:2001.03006v1 [cond-mat.mes-hall] 9 Jan 2020

We present a new automated method for structural classification of the traces obtained in break junction experiments. Using recurrent neural networks trained on the traces of minimal cross-sectional area in molecular dynamics simulations, we successfully separate the traces into two classes: point contact or nanowire. This is done without any assumptions about the expected features of each class. The trained neural network is applied to experimental break junction conductance traces, and it separates the classes as well as the previously used experimental methods. The effect of using partial conductance traces is explored, and we show that the method performs equally well using full or partial traces (as long as the trace just prior to breaking is included). When only the initial part of the trace is included, the results are still better than random chance. Finally, we show that the neural network classification method can be used to classify experimental conductance traces without using simulated results for training, but instead training the network on a few representative experimental traces. This offers a tool to recognize some characteristic motifs of the traces, which can be hard to find by simple data selection algorithms.

We review data analysis techniques that can be used to study temporal correlations among conductance traces in break junction measurements. We show that temporal histograms are a simple but efficient tool to check the temporal homogeneity of the conductance traces, or to follow spontaneous or triggered temporal variations, like structural modifications in trained contacts, or the emergence of single-molecule signatures after molecule dosing. To statistically analyze the presence and the decay time of temporal correlations, we introduce shifted correlation plots. Finally, we demonstrate that correlations between opening and subsequent closing traces may indicate structural memory effects in atomic-sized metallic and molecular junctions. Applying these methods on measured and simulated gold metallic contacts as a test system, we show that the surface diffusion induced flattening of the broken junctions helps to produce statistically independent conductance traces at room temperature, whereas at low temperature repeating tendencies are observed as long as the contacts are not closed to sufficiently high conductance setpoints. Applying opening-closing correlation analysis on Pt-CO-Pt single-molecule junctions, we demonstrate pronounced contact memory effects and recovery of the molecule for junctions breaking before atomic chains are formed. However, if chains are pulled the random relaxation of the chain and molecule after rupture prevents opening-closing correlations.

Abstract. Present paper describes the precursor activity observed in the ASDEX Upgrade tokamak before sawtooth crashes in various neutral beam heated plasmas, utilizing the soft X-ray diagnostic. Besides the well-known (m, n) = (1, 1) internal kink mode and its harmonics, a lower frequency mode is studied in detail. Power modulation of this mode is found to correlate with the power modulation of the (1,1) kink mode in the quasi-stationary intervals indicating possible non-linear interaction. Throughout the studied sawtooth crashes, the power of the lower frequency mode rose by several orders of magnitude just before the crash. Besides its temporal behaviour, its spatial structure was estimated and the most likely value was found to be (1,1). A possible role of this mode in the mechanism of the sawtooth crash is discussed.